Ultrasound is progressing toward becoming an affordable and versatile solution to medical imaging. With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging as it requires trained operators in close proximity to patients for a long period of time, therefore increasing risk of infection. In this work, we investigate the important yet seldom-studied problem of scan target localization, under the setting of lung ultrasound imaging. We propose a purely vision-based, data driven method that incorporates learning-based computer vision techniques. We combine a human pose estimation model with a specially designed regression model to predict the lung ultrasound scan targets, and deploy multiview stereo vision to enhance the consistency of 3D target localization. While related works mostly focus on phantom experiments, we collect data from 30 human subjects for testing. Our method attains an accuracy level of 16.00(9.79) mm for probe positioning and 4.44(3.75) degree for probe orientation, with a success rate above 80% under an error threshold of 25mm for all scan targets. Moreover, our approach can serve as a general solution to other types of ultrasound modalities. The code for implementation has been released.

翻译：超声技术正逐步发展为一种经济且多功能的医学成像解决方案。随着COVID-19全球大流行的出现，完全自动化超声成像的需求日益迫切，因为该技术需要训练有素的操作员长时间与患者近距离接触，从而增加了感染风险。本研究在肺部超声成像的背景下，探讨了扫描目标定位这一重要但鲜有研究的问题。我们提出了一种纯视觉、数据驱动的方法，结合了基于学习的计算机视觉技术。将人体姿态估计模型与专门设计的回归模型相结合，用于预测肺部超声扫描目标，并采用多视图立体视觉技术增强三维目标定位的一致性。虽然相关研究主要聚焦于体模实验，本研究收集了30名人类受试者的数据进行测试。该方法在探头定位精度上达到16.00（9.79）毫米，探头方向精度达到4.44（3.75）度，在所有扫描目标误差阈值25毫米下的成功率超过80%。此外，该方法可作为其他超声模态的通用解决方案。相关实现代码已公开。